【中英双语】关于人工智能的前景，金融业告诉我们什么？

What the Finance Industry Tells Us About the Future of AI

AI在公众认知中的迅速崛起，让许多人好奇由AI主导的未来是什么样的。AI会带来各行各业的变革吗？如果是这样，各行各业将更民主化还是更集中化？AI会带来更好还是更坏的结果？在过去10年里，日益强大的计算能力和丰富的数据不断驱动着AI改造金融世界，我们可以在这里找到基础答案。在AI主导的未来中，金融业的发展历程既鼓舞人心，又发人深省。它表明，AI将为一些(但不是全部)行业带来变革，它将最大程度地惠及大型企业，而且，就像它让企业个体变得更敏捷一样，AI可能会让世界变得更迟钝。

The meteoric rise of artificial intelligence (AI) in the public conscience has caused many people to question what an AI-dominated future looks like. Will AI transform industries? If so, will it democratize or consolidate them? Will it create better or worse outcomes? Outlines of answers can be found in the world of finance which has been transformed in the last decade by the same forces driving AI: the diffusion of ever more powerful computing and the profusion of data. The experience of finance is both encouraging and sobering for an AI-dominated future. It suggests that AI will transform some (but not all) industries, that it will benefit larger players most, and that just as it makes individual players smarter, it may make the world dumber.

信息处理是金融市场的核心功能，因此金融世界就像是探索AI潜在影响的实验室。各类金融机构都在技术和数据方面投入巨资，数额远远领先于其他行业，从而最大化提高竞争效率。当然，新型大型语言模型在过去六个月里给世界留下深刻印象，仅凭金融业的经验可能并不能完全体现它的能力。但如果谈到AI成本下降和广泛应用对于大多数行业意味着什么，从过去10年金融领域不断变化的竞争态势中可窥一斑。无论更新迭代的AI版本如何发挥作用，金融业永远会第一个感知到经济领域的预警信号。

The world of finance is an obvious laboratory for exploring the potential effects of AI because information processing is the central function of financial markets. Unsurprisingly, financial institutions of all types invest heavily in technology and data well ahead of other industries in order to compete most effectively. Of course, the experience of finance may not fully illuminate the scope of newer large language models that have so impressed the world in the last six months. But the changing competitive dynamics within finance over the last decade provide clues about what will happen across many industries when AI becomes cheaper and more widely available. And regardless of how these newer versions of artificial intelligence play out, finance will always to be the canary in the coal mine for the rest of the economy.

首先，AI可以非常迅速地颠覆行业动态，这一点是显而易见的。以资管行业为例，在过去的15年里，我们见证了两次重大危机，这些危机源于技术和数据逐渐显露优势。首先，基金行业经历了被动型基金经理(即投资基于指数而非基于分析的基金经理)的崛起，以及主动型基金经理(即选股者)的衰落。数据和技术的加持使被动投资更具竞争力，主动投资经理更难获得信息优势，局势迅速转变。仅在过去8年里，被动管理资产与主动管理资产的比例就从0.6上升至1.2，市场份额发生了巨大变化。在此之前，主动型基金经理收取的高额管理费相当于所管理资产的一个百分点以上，如今这项收入已受到重创，因为被动型基金经理证明，他们仅需十分之一的成本，就有能力做到与许多主动型基金管理策略相差无几。

First, it appears clear that AI can disrupt industry dynamics very quickly. Consider the asset management industry. Over the last 15 years, we have witnessed two significant disruptions that can be traced to the growing dominance of technology and data. First, the mutual fund industry has seen the rise of passive fund managers (i.e., managers who invest in indices with no analysis) and the decline of active fund managers (i.e., stock pickers). This shift has occurred remarkably quickly as data and technology made passive investing more competitive and made it more difficult for active managers to attain informational edges. In the last eight years alone, the ratio of passively-managed assets to actively managed assets has risen from 0.6 to 1.2 — a dramatic shift in market share. The ability of active fund managers to extract large fees (upwards of one percentage point of assets under management) has been clobbered as passive fund managers demonstrated their ability to approximate many active fund management strategies at one-tenth of the cost.

其次，量化投资逐渐取代了以基本面为导向的传统多空策略，对冲基金行业因此发生了变化。传统经验中，作出多空投资决策需要更慢、更深入的分析，快速分析大量数据并创建相对短期策略的能力似乎已经推倒前浪。金融领域的这些趋势表明，AI主导的未来中，成败仅在一瞬间。

Second, the hedge fund industry has been transformed by the growing dominance of quantitative investing over traditional, fundamentals-driven long-short strategies. The ability to analyze large amounts of data quickly and create relatively short-term strategies appears to be beating the slower and deeper analysis that traditionally led to long and short investment decisions. These trends in finance suggests that an AI-dominated future can create outsized winners and losers in very short order.

金融界的经验也表明，并非所有变化都像人们预想的那样快。虽然金融交易瞬息万变，导致宏观经济、市场情绪及公司特定信息迅速发生变化，但财富管理和信贷行业受到的影响相对较小。

At the same time, the experience of the financial world suggests that not everything changes as quickly as people predict. While the high-frequency world of financial trading with its confluence of macroeconomic, sentiment, and company-specific information has changed rapidly, the lower frequency worlds of wealth management and lending have changed considerably less.

备受期待的机器人顾问曾使庞大的金融咨询机构黯然失色，但这种侵蚀似乎已经停滞，甚至也许正在逆转。看来金融业的客户们仍然更爱人类。同样，AI对信贷的影响也没有预想的那么大，AI贷款机构面临着相当大的问题。处理个人和企业信贷数据增量并不会像泛金融业一样有那么庞大的需求。

The much anticipated ability of robo-advisors to eclipse the massive financial advisory complex has appeared to stall and may be reversing. It appears that the client side of finance retains a preference for humans. Lending, similarly, has not been transformed by AI nearly as much as was predicted and AI-powered lenders have faced considerable problems. The incremental amount of additional data to be processed on individuals and business credit may just not be as large or as useful as in financial markets broadly.

看起来，AI颠覆行业动态的力量与其处理信息问题的性质密切相关。金融市场面对多维信息问题，需要大量的数据和计算能力。在具有类似性质的领域，如药物设计领域，AI应用或已成熟。但在许多其他领域，如服务业和制造业的一些细分领域，似乎与AI没有这样的相关性，更像财富管理或信贷行业那样。金融业的经验表明，对于面向人类的服务，数据没那么丰富，变化又十分迅速，那么就可以在人工智能的世界里很大程度上完整保留下来。需要明确的一点是，AI仍然可以通过改善决策产生巨大影响，但就像在财富管理和信贷领域中那样，AI带来的改变是渐进式的，并非像它在资管领域所作出的颠覆式影响。

The power of AI to disrupt industry dynamics appears to be tightly connected to the nature of the information problems being solved. Financial markets are a multi-dimensional information problem that requires massive amount of data and computing power. Fields with similar properties, like drug design, may be ripe for AI disruption. But many fields, including those in the services sector and manufacturing, simply may not have the same relevance for AI — they may be more like wealth management or lending. The experience of the finance industry suggests that human-facing services where data is not abundant and fast-changing can remain largely intact in a world of AI. To be clear, AI can still have a large impact by improving decision making but it is more likely to be incremental (as it has been in wealth management and lending) rather than transformational (as it has been in money management).

金融世界也可以帮助我们了解AI是使各行业更民主化还是更集中化。这个答案似乎比较明晰。在AI发挥关键作用的领域如金融市场，规模和速度似乎是成功的关键决定因素。当技术和数据占据主导地位时，成功者会越来越成功，针对技术和数据进行投资的实力是拉开差距的关键。相对于老牌企业，规模较小的量化基金在获取数据源和计算能力方面面临重大挑战。同样，被动投资的收入也会继续下降，因为参与者规模越大，与投资者分享越多规模效益，新入局者就会被排挤。。对于AI影响下变化最大的经济领域，可预测规模的大小是决定性因素，如果说大量足以挑战老牌企业的小型企业会逐渐涌现出来的话，未免有些言过其实了。

The world of finance can also help us understand if AI will be democratizing or consolidating. Here, it appears that the answer is less equivocal. Where AI has been pivotal (i.e., in financial markets), scale and speed appear to be the critical determinants of success. When technology and data come to dominate, winners keep winning and the ability to invest in technology and data is the key differentiator. A smaller quant fund has significant challenges in acquiring data feeds and computing power relative to established players. Similarly, fees for passive investing just continue to decrease as larger players share the benefits of scale with investors thereby boxing out upstarts. For sectors of the economy where AI is transformational, scale can be expected to be determinative and hopes for a great unleashing of smaller players that challenge established players appear to be overstated.

关于AI是否对人类有益，金融业的经验能告诉我们什么？在这方面，金融界的经验更为发人深省。那些业绩不尽人意却收取高额费用的主动型经理被取代，似乎是令人拍手称快的进步。但同时，金融市场的核心任务——即信息处理——似乎没有做得更好，而且可能会变得更糟。有意忽视信息的被动投资者和沉迷量化基金的投资者数量不断增加，这意味着处理缓慢、模糊、公司特定信息的艰巨工作或将遭遇忽视。随着数据和计算逐渐占据主导地位，各行业可能会过度依赖快速变化的硬性数据，例如股票价格变动、实时信用卡消费数据。与此同时，软性数据，如公司的未来前景、管理质量、定价策略的长期后果，即使它们对市场更加重要，也可能被削弱和边缘化。

What can the finance industry’s experience tell us about whether AI is good for humans? Here, the experience of the world of finance is more sobering. The displacement of active managers who were charging large amounts for little excess performance seems like a positive development that is worth cheering. At the same time, it does not appear that financial markets are doing their central task — the processing of information — much better and it could be getting worse. The rise of investors that either willfully ignore information (passive investors) or obsess about fast-changing information (quant funds) means that the hard work of processing slow-moving, ambiguous, firm-specific information may be getting neglected. As data and computing come to dominate, industries may come to rely excessively on hard data that is fast-changing (e.g., stock price movements, real time credit card data on spending). Meanwhile, softer data (e.g., the future prospects of firms, the quality of management, the longer run consequences of pricing strategies) can be subordinated and diminished — even if it is what really matters for markets.

总结起来恐怕就是，AI最擅长的是以非结构化方式分析硬性数据的能力，正如它对金融市场做的那样，有望在许多方面改变世界。但这种转变可能仅限于数据丰富且变化迅速的环境。此外，能够对计算能力和数据获取进行投资，以制定差异化战略的大型公司有望成为最终赢家。即使软性数据从长期来看是最重要的，但对其进行考量的能力的溢价在短期内可能会下降。

I fear this last lesson may generalize particularly well. The ability to analyze hard data in unstructured ways that are not directed by humans — the hallmark of AI — promises to transform the world in many ways, just as financial markets have been. But that transformation may be limited to settings where data is abundant and fast-changing. Moreover, the winners will be the largest firms able to invest in the computing power and data to create differentiated strategies. And the premium on the ability to consider softer data could fall in the short run even if, ultimately, it is what matters the most.

金融市场如何利用人工智能奇迹，而不忽视这些更根本的问题，这件事情有解吗？目前达成的一个平衡点是，金融市场由提供相对廉价的大宗商品服务的大型企业主导，却忽视了对软性信息的处理。金融界，或许也是我们所有从业者，面临的挑战是要记住，管理者和领导者面临的最棘手的问题并不完全是由硬性数据决定的。我的企业如何在10年内取得成功？我怎样才能最有效地部署资本，不断创新，创造出能够更好地服务客户的产品和服务？硬数据能够为这些决策提供依据，但无法完全起决定性作用。决策需要想象力和信念。当AI使利用硬数据变得更平价、更高效，判断力会越来越重要。承认这些人的问题排在首位，并不会减少AI对我们的帮助，而只是强调了AI仅仅是一种技术，而对管理者和投资者而言，从根本上来说，人为的努力才能换取最大的回报。

Can financial markets figure out how to capitalize on the wonders of AI and not neglect these more fundamental issues? The current equilibrium appears to be a financial market dominated by large players providing commodity services relatively cheaply but that neglects the processing of softer information. The challenge for the world of finance — and perhaps all of us — is to remember that the hardest questions facing managers and leaders are not entirely determined by hard data. What will allow my enterprise to succeed in 10 years? How can I deploy capital most effectively so that we innovate to create products and services that can serve our customers better? Hard data will inform these decisions but it is unlikely to be entirely dispositive. These decisions require acts of imagination and conviction. Just as the ability to use hard data cheapens and becomes more efficient via AI, it is these acts of judgment that will rise in importance. To acknowledge the primacy of these human questions does not diminish how much AI can help us — it simply reasserts that AI is merely a technology and that the greatest rewards for managers and investors rests in these fundamentally human endeavors.

米希尔· A·德赛 是哈佛商学院瑞穗金融集团金融学教授和哈佛法学院法学教授。